Tumor cell migration characteristics analysis method, device, storage medium and related equipment

Abstract

The tumor cell migration characteristic analysis method, device, storage medium and related equipment provided in the present application can, when analyzing the migration characteristics of tumor cells, first obtain the fluorescent microscopic image corresponding to the tumor cells in the migration channel at each preset acquisition time, so that the number of cells on each sensing electrode in each fluorescent microscopic image can be counted and the statistical results can be obtained; then, the present application can also collect the electrophysiological signals generated by the tumor cells during the migration process captured by each sensing electrode on the microfluidic electrode array chip within the preset acquisition period, and use fast Fourier transform to obtain the power spectral density distribution corresponding to the electrophysiological signal. Based on the statistical results and the power spectral density distribution, the spike signal corresponding to each sensing electrode is extracted from the electrophysiological signal, and the spike occurrence frequency of each sensing electrode is determined; then, based on the statistical results and the spike occurrence frequency of each sensing electrode, the migration characteristics of the tumor cells are determined.

Description

Technical Field

[0001] The present application relates to the technical field of tumor cell migration characteristic analysis, and in particular to a tumor cell migration characteristic analysis method, apparatus, storage medium and related equipment. Background Art

[0002] Cell invasion refers to the ability of cells to migrate from one area to another through the extracellular matrix. Cell invasion occurs in both normal and cancerous cells in response to chemical and mechanical stimuli. Prior to migration to a new location, the extracellular matrix is ​​degraded by intracellular proteases. Cell invasion often occurs in processes such as wound repair, angiogenesis, and inflammatory responses, as well as in abnormal tissue infiltration and tumor metastasis.

[0003] For example, the high invasiveness of malignant gliomas currently hinders clinical treatment, so the study of the invasion mechanism of gliomas is of great significance. Cell invasion is divided into two processes: directional movement and crossing physical barriers. Current studies have elucidated how gliomas change their own cell volume to cross physical barriers, but there is no consensus on why gliomas move in a directional manner towards neural tissue. On the other hand, a large number of studies have shown that gliomas can spontaneously or stimulatedly generate depolarized membrane potentials, and this electrophysiological signal is closely related to the progression of gliomas. In addition, some studies have reported the electrotaxis of gliomas, that is, the phenomenon that direct current electric fields promote the directional movement of glioma cells toward the cathode.

[0004] However, none of the above studies mentioned the specific relationship between the electrophysiological signals of glioma cells and cell invasion. Therefore, it is necessary to study a method to analyze the migration characteristics of tumor cells in order to gain a deeper understanding of the relationship between the electrophysiological signals of tumor cells and cell invasion. Summary of the Invention

[0005] The purpose of this application is to solve at least one of the above technical deficiencies, especially the lack of a technical method for studying the relationship between electrophysiological signals of tumor cells and cell invasion in the prior art.

[0006] The present application provides a method for analyzing tumor cell migration characteristics, the method comprising:

[0007] Acquire a fluorescence microscopic image corresponding to the tumor cells in the migration channel at each preset acquisition time, wherein the migration channel is a cell migration channel on the microfluidic electrode array chip connected to the culture chamber, extending from the first sensing electrode to the last sensing electrode and extending to the reference electrode;

[0008] Counting the number of cells on each sensing electrode in each fluorescence microscopy image and obtaining statistical results;

[0009] collecting electrophysiological signals generated by tumor cells during migration, captured by each sensing electrode on the microfluidic electrode array chip within a preset collection period, and obtaining a power spectral density distribution corresponding to the electrophysiological signals using fast Fourier transform;

[0010] extracting spike signals corresponding to the respective sensing electrodes from the electrophysiological signals based on the statistical results and the power spectrum density distribution, and determining the spike occurrence frequency of the respective sensing electrodes;

[0011] The migration characteristics of the tumor cells are determined according to the statistical results and the peak occurrence frequency of each sensing electrode.

[0012] Optionally, extracting the spike signal corresponding to each sensing electrode from the electrophysiological signal based on the statistical result and the power spectrum density distribution, and determining the spike occurrence frequency of each sensing electrode includes:

[0013] determining an energy concentration frequency range of the electrophysiological signal based on the power spectral density distribution;

[0014] Filtering the electrophysiological signal using a filter, and extracting a signal to be processed corresponding to the energy concentration frequency range from the filtered electrophysiological signal;

[0015] The peak signal corresponding to each sensing electrode is extracted from the signal to be processed based on the statistical result, and the peak occurrence frequency of each sensing electrode is determined.

[0016] Optionally, extracting the peak signal corresponding to each sensing electrode from the signal to be processed based on the statistical result and determining the peak occurrence frequency of each sensing electrode includes:

[0017] Determine the correction threshold when extracting spike signals;

[0018] Use the findpeaks function in Matlab to find the extreme value of the signal to be processed whose absolute value is greater than the correction threshold;

[0019] After eliminating the extreme values ​​that do not meet the preset extreme value specifications from the found extreme values, an initial peak signal corresponding to each sensing electrode is obtained;

[0020] After removing abnormal peaks from each initial peak signal according to the statistical result, the peak occurrence frequency of each sensing electrode is determined according to the preset acquisition period and the initial peak signal after removing the abnormal peaks.

[0021] Optionally, the calculation process of determining the correction threshold when extracting the peak signal is as follows:

[0022]

[0023]

[0024]

[0025]

[0026] Among them, in formula (1) to formula (4), and are the arithmetic mean and standard deviation of the electrophysiological signals, The experience value is 4, , is the electrophysiological signal captured by the i-th sensing electrode, .

[0027] Optionally, determining the migration characteristics of the tumor cells according to the statistical results and the peak occurrence frequency of each sensing electrode includes:

[0028] Determine the total number of cells on all sensing electrodes and the total spike frequency collected per minute on all sensing electrodes based on the statistical results and the spike frequency of each sensing electrode;

[0029] The relationship between the number of cells of the tumor cells and the spike frequency is analyzed based on the number of cells on each sensing electrode and the corresponding spike frequency, the total number of cells on all sensing electrodes and the corresponding total spike frequency.

[0030] Optionally, determining the migration characteristics of the tumor cells according to the statistical results and the peak occurrence frequency of each sensing electrode includes:

[0031] Determining, according to the peak occurrence frequency of each sensing electrode, an average value of the peak occurrence positions in the migration channel within a preset statistical period;

[0032] determining an average value of the positions of all tumor cells in the migration channel at a preset statistical moment according to the statistical results;

[0033] The relationship between the spatial distribution of the tumor cells and the frequency of spike occurrence is analyzed based on the average value of the positions of the spikes in the migration channel and the average value of the positions of all tumor cells in the migration channel.

[0034] Optionally, the calculation formula for determining the average value of the positions where the peaks occur in the migration channel within a preset statistical period according to the peak occurrence frequency of each sensing electrode is:

[0035]

[0036] Where i is the number of sensing electrodes, n is the number of sensing electrodes, is the frequency of spikes on the i-th sensing electrode, and d is the center distance between adjacent electrodes;

[0037] The calculation formula for determining the average value of the positions of all tumor cells in the migration channel at a preset statistical moment based on the statistical results is:

[0038]

[0039] Where i is the number of sensing electrodes, n is the number of sensing electrodes, is the number of cells on the i-th sensing electrode, and d is the center distance between adjacent electrodes.

[0040] The present application also provides a device for analyzing tumor cell migration characteristics, comprising:

[0041] An image acquisition module, configured to acquire a fluorescence microscopic image corresponding to tumor cells in the migration channel at each preset acquisition moment, wherein the migration channel is a cell migration channel on the microfluidic electrode array chip connected to the culture chamber, extending from the first sensing electrode to the last sensing electrode and extending to the reference electrode;

[0042] The cell statistics module is used to count the number of cells on each sensing electrode in each fluorescence microscopy image and obtain statistical results;

[0043] a power calculation module for collecting electrophysiological signals generated by tumor cells during migration, captured by each sensing electrode on the microfluidic electrode array chip within a preset collection period, and obtaining a power spectral density distribution corresponding to the electrophysiological signals using a fast Fourier transform;

[0044] a spike signal extraction module, configured to extract, from the electrophysiological signal, spike signals corresponding to the respective sensing electrodes based on the statistical results and the power spectrum density distribution, and determine the spike occurrence frequency of the respective sensing electrodes;

[0045] The migration characteristic analysis module is used to determine the migration characteristics of the tumor cells according to the statistical results and the peak occurrence frequency of each sensing electrode.

[0046] The present application also provides a computer-readable storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the tumor cell migration characteristic analysis method as described in any of the above embodiments.

[0047] The present application also provides a computer device, comprising: one or more processors, and a memory;

[0048] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method for analyzing tumor cell migration characteristics as described in any one of the above embodiments are performed.

[0049] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0050] The method, device, storage medium and related equipment for analyzing the migration characteristics of tumor cells provided by the present application, when analyzing the migration characteristics of tumor cells, since the migration channel of the present application is a cell migration channel connected to the culture chamber on the microfluidic electrode array chip, extending from the first sensing electrode to the last sensing electrode and extending to the reference electrode, it is possible to first obtain a fluorescence microscopic image corresponding to the tumor cells in the migration channel at each preset acquisition time, so that the number of cells on each sensing electrode in each fluorescence microscopic image can be counted and statistical results can be obtained; then, the present application can also collect the fluorescence microscopic images of each sensing electrode on the microfluidic electrode array chip at the time of the acquisition. The electrophysiological signals generated by tumor cells during their migration are captured within a preset acquisition period, and the power spectral density distribution corresponding to the electrophysiological signals is obtained using fast Fourier transform. In this way, based on the statistical results and the power spectral density distribution, the spike signals corresponding to each sensing electrode can be extracted from the electrophysiological signals, and the spike occurrence frequency of each sensing electrode can be determined. Then, based on the statistical results and the spike occurrence frequency of each sensing electrode, the migration characteristics of the tumor cells can be determined, such as analyzing the relationship between the number of cells and the spike occurrence frequency, the relationship between the spatial distribution of cells and the spike occurrence frequency, etc., thereby studying the migration characteristics of tumor cells from multiple aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0052] Figure 1 A schematic diagram of a process for analyzing tumor cell migration characteristics provided in an embodiment of the present application;

[0053] Figure 2 A schematic diagram of the structure of a microelectrode array provided in an embodiment of the present application;

[0054] Figure 3 A schematic diagram of the working process of the microfluidic electrode array chip provided in an embodiment of the present application;

[0055] Figure 4Schematic diagram of a fluorescence microscopic image 24 hours after tumor cell implantation provided in an embodiment of the present application;

[0056] Figure 5 A schematic diagram showing the distribution of the frequency of spikes on each sensing electrode over time provided in an embodiment of the present application;

[0057] Figure 6 A schematic diagram showing the change of the total peak occurrence frequency over time provided in an embodiment of the present application;

[0058] Figure 7 This is a diagram showing the results of time domain and frequency domain analysis of electrophysiological signals of glioma cells provided in an embodiment of the present application;

[0059] Figure 8 A graph showing the results of extracting spike signals according to an embodiment of the present application;

[0060] Figure 9 A graph showing the relationship between the number of cells and the frequency of spikes provided in the examples of this application;

[0061] Figure 10 A graph showing the relationship between the spatial distribution of cells and the frequency of spikes provided in the examples of this application;

[0062] Figure 11 A schematic diagram of the structure of a tumor cell migration characteristic analysis device provided in an embodiment of the present application;

[0063] Figure 12 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0065] In one embodiment, Figure 1 As shown, Figure 1 This is a flow chart of a method for analyzing tumor cell migration characteristics provided in an embodiment of the present application. The present application provides a method for analyzing tumor cell migration characteristics, which may include:

[0066] S110: Acquire a fluorescence microscopic image corresponding to the tumor cells in the migration channel at each preset acquisition moment.

[0067] S120: Counting the number of cells on each sensing electrode in each fluorescence microscopic image and obtaining a statistical result.

[0068] In the above steps, when analyzing the cell migration characteristics, the corresponding fluorescence microscopic image of the tumor cells in the migration channel at each preset acquisition time can be obtained first. In this way, the number of cells on each sensing electrode can be counted through the fluorescent fiber image to obtain statistical results.

[0069] The migration channel of the present application is a cell migration channel on a microfluidic electrode array chip connected to a culture chamber, extending from the first sensing electrode to the last sensing electrode and extending to the reference electrode. Specifically, the microfluidic electrode array chip of the present application may include a glass substrate, a microelectrode array distributed on the surface of the glass substrate, and a microfluidic structure bonded to the surface of the microelectrode array; wherein, Figure 2 As shown, Figure 2 A schematic diagram of the structure of a microelectrode array provided in an embodiment of the present application; Figure 2 The microelectrode array of the present application may include a plurality of evenly spaced sensing electrodes, a reference electrode remote from the distal sensing electrodes, wires connected to each sensing electrode and reference electrode, and lead probes connected to the distal ends of each wire. Each lead probe is distributed near the corresponding sensing electrode in a shortest path manner, which facilitates wiring and avoids circuit redundancy. Furthermore, the present application connects the reference electrode to ground and places it as far away from the wires and sensing electrodes as possible, thereby increasing the impedance between the reference electrode and the sensing electrode and improving the insulation of the wires from ground.

[0070] Furthermore, if Figure 3 As shown, Figure 3 Schematic diagram of the working process of the microfluidic electrode array chip provided in the embodiment of the present application; wherein, Figure 3 The diagrams of AC in the figure are the working principle of the microfluidic electrode array chip. Figure 3 As can be seen from ac in the figure, the microfluidic structure of the present application is bonded to the surface of the microelectrode array, and includes a culture chamber arranged on one side of the first sensing electrode, a migration channel connected to the culture chamber and extending from the first sensing electrode to the reference electrode, and an air pump sealed to the end of the migration channel, wherein the culture chamber is connected to the atmosphere, allowing the pipette to load cells and culture medium directly from above. The air pump can change the air pressure in the migration channel. The air pump of the present application is preferably a syringe. When using a syringe as an air pump, the cells and culture medium are first loaded into the culture chamber, and then the syringe is squeezed so that the liquid medium cannot enter the migration channel under the action of air pressure, and the cell monolayer is only established in the culture chamber; then, the syringe is evacuated to allow the culture medium in the culture chamber to enter the migration channel. The present application can then cut off the hose at the air pump interface to release the air pressure closure in the migration channel, and the cells begin to migrate.

[0071] During the process of cell migration, the present application can collect the fluorescence microscopic images corresponding to the tumor cells in the migration channel when the preset sampling time arrives. For example, the present application can use a fluorescence inverted microscope to take fluorescence microscopic images of tumor cells in the migration channel every 24 hours (4x microscope, excitation light wavelength 488 nm). The number of fluorescence microscopic images taken can be set according to the actual situation and is not limited here. After taking the fluorescence microscopic image, the present application can count the number of tumor cells on each sensing electrode in the fluorescence microscopic image in imageJ. Schematically, as shown in FIG. Figure 4 As shown, Figure 4 Schematic diagram of fluorescence microscopy image 24 hours after tumor cell implantation provided in the embodiment of the present application; Figure 4 It can be seen that the cells at 0 h stayed in front of the first electrode, while the cells at 24 h had reached the sixth electrode at most, with a cell migration speed of about 50 μm / h. Figure 4 The final statistical result can be obtained by counting the number of cells on each sensing electrode in the fluorescence microscopic image. It is understandable that the tumor cells in this application can be glioma cells or other tumor cells with similar cell characteristics to glioma cells, and this is not limited here.

[0072] Furthermore, before obtaining the corresponding fluorescence microscopic images of tumor cells in the migration channel at each preset acquisition time, the present application can also prepare corresponding tumor cells according to experimental requirements and plant the tumor cells in the culture chamber of the microfluidic electrode array. The pre-processing process of the present application is as follows:

[0073] 1. Cell Recovery

[0074] Thaw the cryovial containing 1 mL of cell suspension by rapid shaking in a 37°C water bath. Add 4 mL of culture medium and mix thoroughly. Centrifuge at 1000 rpm for 3 minutes, discard the supernatant, add 1-2 mL of culture medium, and mix thoroughly. Then, add the entire cell suspension to a 6 cm culture dish, add approximately 4 mL of culture medium, and culture overnight. Change the medium the next day and check the cell density.

[0075] 2. Cell Passaging

[0076] If the cell density reaches 80%-90%, subculture can be performed. First, discard the culture supernatant and rinse the cells 1-2 times with PBS without calcium and magnesium ions. Then add 1mL of digestion solution (0.25% Trypsin-0.53mM EDTA) to the culture dish to allow the digestion solution to infiltrate all cells. Place the culture dish in a 37°C incubator for digestion for 1-3 minutes, then observe the cell digestion under a microscope. If most of the cells become round and fall off, quickly return to the operating table and add 2-3ml of complete culture medium to terminate digestion. After gently mixing, place it in a sterile centrifuge tube, centrifuge at 1000rpm for 5 minutes, discard the supernatant, add 1-2mL of culture medium and blow evenly. Finally, divide the cell suspension into a new dish containing 8mL of culture medium at a ratio of 1:2 and place it in an incubator for culture.

[0077] 3. Cell Cryopreservation

[0078] When the cells are growing well, they can be frozen. First, collect the cells and cell culture medium, place them in a sterile centrifuge tube, centrifuge at 1000 rpm for 4 minutes, discard the supernatant, wash once with PBS, discard the PBS, and count the cells. Add serum-free cell freezing solution according to the number of cells to make the cell density 5×10 6 Gently mix and freeze 1 mL of cell suspension per cryovial. Place the cryovial in a -80°C freezer and transfer to a liquid nitrogen tank after 24 hours. Record the cryovial location for easy access.

[0079] 4. Equipment pretreatment

[0080] This step is used to sterilize the chip, clean the surface, and modify the cell adhesion factors before cell seeding. Soak the microfluidic electrode array chip in 75% alcohol overnight, take it out and dry it to obtain a sterile device. Plasma clean the chip in an oxygen environment for 60 seconds to destroy and remove organic pollutants on the chip surface. Prepare a 1mg / ml poly-L-lysine (PLL) solution with sterile tissue culture water and evenly coat the chip surface in the migration channel (1ml / 25 After waiting for five minutes, remove the surface solution and rinse thoroughly with sterile tissue culture water. Place the chip in a clean bench and dry for 2 hours before proceeding to the next step.

[0081] 5. Cell Seeding

[0082] Add 70 μl of complete medium to the culture chamber and squeeze the syringe to maintain the medium within the chamber under air pressure. Carefully inject a suspension containing 40,000 cells into the chamber and top up to 300 μl with complete medium. Place the device in the incubator and allow the cells to attach after 6 hours. Then, replace half of the medium in the cell chamber every 12 hours using a 100 μl pipette. Repeat this process twice for 7 days.

[0083] 6. Cell Migration

[0084] Carefully draw the culture medium with a syringe until the entire migration channel is filled, cut the hose at the air pump interface to release the air pressure seal in the migration channel, and the cells begin to migrate.

[0085] S130: Collecting electrophysiological signals generated by the tumor cells during migration, which are captured by the sensing electrodes on the microfluidic electrode array chip within a preset collection period, and obtaining a power spectral density distribution corresponding to the electrophysiological signals using fast Fourier transform.

[0086] In this step, after counting the number of cells on each sensing electrode in each fluorescence microscopic image through S110 and S120 and obtaining the statistical results, the present application can also collect the electrophysiological signals generated by the tumor cells during the migration process captured by each sensing electrode on the microfluidic electrode array chip within a preset collection period, and use fast Fourier transform to obtain the power spectral density distribution corresponding to the electrophysiological signal, so that the corresponding signal can be extracted for analysis based on the power spectral density distribution.

[0087] It is understood that in the microfluidic electrode array of the present application, after the tumor cells migrate into the migration channel, the substrate to which they are attached is embedded with microelectrodes, so that the electrophysiological signals during cell migration can be recorded. Figure 3 As shown, Figure 3 d in the figure is a design drawing of a fence-shaped microelectrode array; in d, the dark blue part is a glass substrate, the yellow part is a sensing electrode, and the light blue part is a microfluidic structure. During the migration of cells, the electrophysiological signals generated during the migration process can be recorded through the sensing electrodes at the bottom. Furthermore, the present application can also collect the electrophysiological signals through an electrophysiological signal acquisition system. Specifically, the electrophysiological signal acquisition system of the present application can include a microfluidic electrode array chip, a connector, a front-end probe, an acquisition board, and a host computer; after the microfluidic electrode array chip collects multi-channel electrophysiological signals, it is sent to the front-end probe through a connector. The front-end probe processes the multi-channel analog signals and outputs digital signals. The acquisition board adds a public timestamp to the digital signals sent by the front-end probe, integrates them, and sends them to the host computer. The signals are then visualized and stored in the host computer software.

[0088] Further, Figure 3The e in the figure is a schematic diagram of the process of collecting electrical signals of migrating cells; Figure 2 As can be seen, the present invention arranges multiple sensing electrodes evenly spaced on a glass substrate, enabling long-term, low-noise monitoring of the spatiotemporal distribution of electrophysiological signals from cell clusters without interfering with tumor cell movement. Furthermore, the present invention places the reference electrode away from the distal sensing electrodes, which not only increases the impedance between the reference and sensing electrodes but also improves the insulation of the wires from ground.

[0089] After the present application collects the electrophysiological signals generated by the tumor cells during migration captured by the various sensing electrodes on the microfluidic electrode array chip within a preset collection period, the power spectral density distribution corresponding to the electrophysiological signals can be obtained using fast Fourier transform.

[0090] Specifically, after absorbing 100 μl of culture medium in the culture chamber and placing the sterilized lid on the culture chamber, the present application can install the microfluidic electrode array chip in the connector for collecting the electrophysiological signal, wait for the liquid level to stabilize, record the electrical signal for 610 seconds, and then remove the chip. Remove the lid in the biosafety cabinet, add 200 μl of complete culture medium to the culture chamber, and put the chip back into the incubator. The above steps are performed once every 24 hours from the time the cells attach to the wall, and repeated for 7 days. For all the above operations, the day the cells are planted is recorded as Day 0; the first day after the cell migration begins is recorded as Day 1, and so on. In this application, the host computer software in the electrophysiological signal acquisition system stores the signals in binary format.

[0091] Furthermore, since the sampling rate of the original data of this application is 30kHz, in order to compress the data volume and reduce the computing cost, this application can also resample the original data. According to the Nyquist sampling theorem, in order to avoid signal distortion, the new sampling frequency should be greater than 2 times the maximum frequency of the signal, usually 5 to 10 times. For example, after using the fast Fourier transform to obtain the power spectral density distribution of the typical glioma electrical signal, this application observed that the main component of the signal is below 30Hz. Therefore, the resampling frequency can be set to 3kHz, thereby satisfying the Nyquist sampling theorem.

[0092] S140: Based on the statistical results and the power spectrum density distribution, extract the spike signal corresponding to each sensing electrode from the electrophysiological signal, and determine the spike occurrence frequency of each sensing electrode.

[0093] In this step, after obtaining the power spectral density distribution corresponding to the electrophysiological signal by fast Fourier transform in S130, the present application can extract the spike signal corresponding to each sensing electrode from the electrophysiological signal based on the statistical results in S120 and the power spectral density distribution in S130, and determine the spike occurrence frequency of each sensing electrode.

[0094] It should be noted that when studying the invasive characteristics of glioma cells, the present application found that the spike center is a potential marker for predicting the direction of glioma invasion. In addition, the study found that the number of spikes and the number of cells in the local space showed a nonlinear correlation, while the two were linearly correlated as a whole. This shows that the discharge frequency between glioma cells is different, and the proportion of the number of each type of cell is basically consistent. At the same time, the spike center and the cell growth center increase logarithmically over time, and there is a linear positive correlation between the two, and the spike center has a 97.5% probability of appearing at the distal end of the migration of the cell growth center. This shows that the spike center may be a precursor to the cell growth center. The above results prove that the invasion of glioma is closely related to spontaneous discharge activity, and the spike center is a potential predictive marker for the direction of glioma invasion.

[0095] Based on this, this application defines the baseline signal as the long-term random fluctuation within a low amplitude range (±10μV) in the collected electrophysiological signal, and locates the spike signal as the short-term fluctuation signal with an amplitude far higher than the baseline signal. The spike signal in this application includes the maximum and minimum values ​​of the signal. After extracting the spike signal corresponding to each sensing electrode from the electrophysiological signal based on the power spectral density distribution, this application can also remove abnormal spikes that appear on the cell-free electrode based on the number of cells in the statistical results, and finally determine the peak location.

[0096] Next, the present application can also determine the spike occurrence frequency of each sensing electrode based on the extracted spike signal, wherein the spike occurrence frequency of a single electrode (Spike Number per min) refers to the number of spikes collected by a single sensing electrode per minute, and the spike number can be determined based on the number of spike signals generated by a single sensing electrode per minute.

[0097] S150: Determine the migration characteristics of the tumor cells based on the statistical results and the peak occurrence frequency of each sensing electrode.

[0098] In this step, after counting the number of cells on each sensing electrode in each fluorescence microscopy image in S120 and obtaining the statistical results, and determining the peak occurrence frequency of each sensing electrode in S140, the present application can determine the migration characteristics of tumor cells based on the statistical results and the peak occurrence frequency of each sensing electrode.

[0099] Schematically, as Figure 5 、 Figure 6 As shown, Figure 5 This is a schematic diagram of the distribution change of the peak occurrence frequency on each sensing electrode over time provided by the embodiment of the present application. Figure 6 A schematic diagram showing the change of the total peak occurrence frequency over time provided in an embodiment of the present application; Figure 6The relationship between the glioma spike frequency of all sensing electrodes and time is shown. After excluding the gray abnormal points, the spike frequency shows a linear growth trend over time. Figure 5 The temporal changes in the frequency of glioma spikes at each electrode are shown. These results demonstrate that glioma cells exhibit discharge activity on the microfluidic electrode array chip, and that the overall discharge frequency of the sample increases with prolonged culture time.

[0100] In the above embodiment, when analyzing the migration characteristics of tumor cells, since the migration channel of the present application is a cell migration channel connected to the culture chamber on the microfluidic electrode array chip, extending from the first sensing electrode to the last sensing electrode and to the reference electrode, a fluorescence microscopic image corresponding to the tumor cells in the migration channel at each preset acquisition time can be first obtained. In this way, the number of cells on each sensing electrode in each fluorescence microscopic image can be counted and a statistical result can be obtained. Next, the present application can also collect electrophysiological signals generated by the tumor cells during migration, captured by each sensing electrode on the microfluidic electrode array chip within a preset acquisition period, and use fast Fourier transform to obtain a power spectral density distribution corresponding to the electrophysiological signal. In this way, based on the statistical results and the power spectral density distribution, the spike signal corresponding to each sensing electrode can be extracted from the electrophysiological signal, and the spike occurrence frequency of each sensing electrode can be determined. Then, based on the statistical results and the spike occurrence frequency of each sensing electrode, the migration characteristics of the tumor cells can be determined, such as analyzing the relationship between the number of cells and the spike occurrence frequency, the relationship between the spatial distribution of cells and the spike occurrence frequency, etc., thereby studying the migration characteristics of tumor cells in multiple aspects.

[0101] In one embodiment, in S140, extracting the spike signal corresponding to each sensing electrode from the electrophysiological signal based on the statistical result and the power spectrum density distribution, and determining the spike occurrence frequency of each sensing electrode may include:

[0102] S141: Determine the energy concentration frequency range of the electrophysiological signal based on the power spectrum density distribution.

[0103] S142: Filter the electrophysiological signal using a filter, and extract a signal to be processed corresponding to the energy concentration frequency range from the filtered electrophysiological signal.

[0104] S143: extracting peak signals corresponding to the respective sensing electrodes from the signal to be processed based on the statistical result, and determining the peak occurrence frequency of the respective sensing electrodes.

[0105] In this embodiment, since the electrophysiological signals of tumor cells, such as glioma cells, lack a research paradigm and the pattern of their effective signals is unknown, the present application can observe the collected electrophysiological signals in the time domain and frequency domain to extract the peak signal that can characterize the invasion direction of the tumor cells.

[0106] In a specific example, Figure 7 As shown, Figure 7 The results of time domain and frequency domain analysis of electrophysiological signals of glioma cells provided in the embodiment of the present application are shown in the figure; wherein, Figure 7 a in the figure is the time-frequency domain analysis of glioma electrical signals: a typical glioma electrical signal segment; Figure 7 b in the figure is the power spectrum density of 289 glioma electrical signals; Figure 7 Where c is the frequency distribution histogram of the power spectrum density peak; Figure 7 d in the figure is the time domain waveform of the typical glioma electrical signal at the main frequency; Figure 7 The e in the figure is the filtering result of a typical spike signal.

[0107] In the time domain, Figure 7 Figure a shows a typical glioma electrical signal. We found that glioma electrical signals mainly consist of two components: long-term random fluctuations within a low amplitude range (±10μV), and short-term fluctuations with amplitudes much higher than the former. We define the former as the "baseline" and the latter as the "spike." In the frequency domain, frequency domain analysis of 289 600s glioma electrical signals showed that ( Figure 7 b), the energy of glioma electrical signals is concentrated in four frequency ranges: 0~30Hz, 450Hz~500Hz, 960Hz~1200Hz and 1400Hz~1500Hz ( Figure 7 c in the figure). Extract the above frequency components of the glioma electrical signal and observe the time domain results ( Figure 7 d in the figure), we can see that the spike signal mainly exists between 0 and 30 Hz. Furthermore, the power spectral density distribution shows that the energy of the glioma signal is mainly distributed below 30 Hz. Therefore, this application can design a low-pass Butterworth IIR filter with a low-pass cutoff frequency of 30 Hz and an order of 4. We also use the filtfilt function in Matlab to implement non-causal zero-phase filtering to eliminate the nonlinear phase distortion of the IIR filter. Figure 7 Figure e shows the filtering results of a typical spike. The gray and red waveforms represent the signals before and after filtering, respectively. After filtering, the noise superimposed on the spike is significantly reduced, and there is no distortion or offset of the spike, demonstrating the effective filtering effect.

[0108] Next, the present application can extract the signal to be processed corresponding to the energy concentration frequency range from the filtered electrophysiological signal, and remove the abnormal spikes appearing on the cell-free electrode based on the number of cells in the statistical results to obtain the final spike signal. The spike signal can be used to determine the spike occurrence frequency of each sensing electrode.

[0109] It should be noted that the above embodiment mainly illustrates the spike signal extraction process for glioma cells. Of course, the present application can also obtain the energy concentration frequency range of the electrophysiological signal corresponding to other types of cells through experiments, and use a filter to filter the electrophysiological signal. Then, the signal to be processed corresponding to the energy concentration frequency range is extracted from the filtered electrophysiological signal. Based on the statistical results, the spike signal corresponding to each sensing electrode is extracted from the signal to be processed, and the spike occurrence frequency of each sensing electrode is determined. The specific implementation process is similar to that of glioma cells and is not limited here.

[0110] In one embodiment, extracting the peak signal corresponding to each sensing electrode from the signal to be processed based on the statistical result and determining the peak occurrence frequency of each sensing electrode in S143 may include:

[0111] S1431: Determine the correction threshold when extracting the peak signal.

[0112] S1432: Use the findpeaks function in Matlab to find an extreme value in the signal to be processed whose absolute value is greater than the correction threshold.

[0113] S1433: After eliminating the extreme values ​​that do not meet the preset extreme value specifications from the found maximum values, an initial peak signal corresponding to each sensing electrode is obtained.

[0114] S1434: After removing abnormal peaks from each initial peak signal according to the statistical result, the peak occurrence frequency of each sensing electrode is determined according to the preset acquisition period and the initial peak signal after removing the abnormal peaks.

[0115] In this embodiment, in neural signal processing, the conventional approach to extracting spikes is to set a threshold based on the estimated background noise amplitude and consider the portion with an absolute value above the threshold as a spike. The threshold determines the quality of spike extraction. Compared to neural signals, there are three difficulties in processing glioma electrical signals: (1) the shape of the spike is unknown, and the threshold lacks a reference value; (2) the frequency component of the spike is low, close to the power frequency, so there is a lot of noise after filtering; (3) the spike amplitude varies greatly. Under the influence of a high-amplitude spike, the threshold may be higher than that of a low-amplitude spike.

[0116] To address the above issues, the present application improves upon conventional methods and designs a modified threshold to eliminate the effects of spikes. Next, the present application uses the findpeaks function in Matlab to find the maximum values ​​in the electrophysiological signal whose absolute values ​​are greater than the modified threshold, i.e., peaks. To prevent noise fluctuations superimposed on the spikes from interfering with the results, the present application can also remove those found that do not meet the preset extreme value specifications. For example, the present application can exclude maximum values ​​with a spacing less than 0.2 s, a width less than 0.01 s, or a height-to-duration ratio less than 5. Finally, the present application can invert the entire signal and repeat the above steps to find the location of the valley. Combining the peak and valley locations, the initial spike signals corresponding to each sensing electrode in the present application can be obtained. Furthermore, the present application can remove abnormal spikes appearing on cell-free electrodes based on the number of cells on each sensing electrode in the fluorescence microscopy image to obtain the final spike signal. Furthermore, since the spike occurrence frequency of a single electrode in the present application refers to the number of spikes collected per minute by a single sensing electrode, and the number of spikes can be determined based on the number of spike signals generated per minute by a single sensing electrode, the present application can determine the spike occurrence frequency corresponding to each sensing electrode based on a preset collection period and an initial spike signal after removing abnormal spikes.

[0117] In one embodiment, the calculation process of determining the correction threshold when extracting the peak signal is as follows:

[0118]

[0119]

[0120]

[0121]

[0122] Among them, in formula (1) to formula (4), and are the arithmetic mean and standard deviation of the electrophysiological signals, The experience value is 4, , is the electrophysiological signal captured by the i-th sensing electrode, .

[0123] In this embodiment, the conventional approach to extracting spikes in neural signal processing is to set a threshold based on the estimated value of the background noise amplitude, and to treat the portion with an absolute value higher than the threshold as a spike, wherein the threshold determines the quality of spike extraction. Therefore, when improving the conventional method, the present application can first select the method with the best background noise estimation effect to perform background noise estimation. For example, the present application can assume that the background noise follows a Gaussian distribution, so that the distribution parameters (i.e., the mean and standard deviation) of the current data can be calculated by maximum likelihood estimation. Spikes are outlier points that do not conform to the background noise distribution, and data with an absolute value greater than the threshold is considered an outlier.

[0124] However, in practice, spike signals as outliers are easily included in the observed data, causing the spike signals to interfere with the probability distribution estimation of the background noise. Figure 8 As shown, Figure 8 This is a graph showing the results of extracting spike signals provided in the embodiment of the present application; wherein, Figure 8 Figure a shows that the traditional threshold is used to extract peaks, which results in the omission of some peaks. After the spike signal extraction algorithm is improved in this application, the modified threshold (red) reduces the number of missed peaks compared to the original threshold (gray). Figure 8 Figure b shows the Gaussian distribution fitting results of the potential frequency distribution of glioma electrical signals under two threshold methods. Figure 8 Figure c shows the absolute mean error comparison of the number of spikes extracted by the two threshold methods. Figure 8 As shown in b in the figure, although the potential of the signal shows a Gaussian distribution trend, there are many spike signals in the signal, and the spikes do not obey the probability distribution of the background noise. This means that in the process of estimating the background noise distribution, the spike signals will interfere with the maximum likelihood estimation, and the more spikes there are, the greater the interference.

[0125] Therefore, the present application designs the above calculation method to determine the correction threshold when extracting the spike signal; in formula (1) to formula (4), and Data points exceeding the threshold were excluded to eliminate the influence of spikes on the estimation of the background noise distribution. Figure 8 Figure b shows the Gaussian distribution fit results for background noise using both the traditional thresholding method (raw, gray) and the modified thresholding method (modified, red). Due to the large number of valleys in the sample signal, the traditional thresholding method shifts the background noise distribution toward the negative end, while the modified thresholding method eliminates this deviation. Figure 8Figure c shows the mean absolute error (MAE) between the number of spikes and the true value for the traditional threshold (blue) and the modified threshold (red). The error bars represent the sample standard deviation (SD). The results show that compared to the traditional threshold, the modified threshold proposed in this application, which eliminates the influence of spikes, achieves a MAE reduction of approximately 17% and is more stable.

[0126] In one embodiment, determining the migration characteristics of the tumor cells according to the statistical results and the peak occurrence frequency of each sensing electrode in S150 may include:

[0127] S151: Determine the total number of cells on all sensing electrodes and the total spike frequency collected per minute on all sensing electrodes according to the statistical results and the spike frequency of each sensing electrode.

[0128] S152: Analyze the relationship between the number of tumor cells and the spike frequency according to the number of cells on each sensing electrode and the corresponding spike frequency, the total number of cells on all sensing electrodes and the corresponding total spike frequency.

[0129] In this embodiment, based on the pre-acquired statistical results and the spike occurrence frequency of each sensing electrode, the present application can analyze the relationship between the number of cells and the spike occurrence frequency in the entire migration channel and on each sensing electrode respectively. Among them, the present application can define the total cell number (Total Cell Number) as the number of cells on all sensing electrodes in a sample; the total spike occurrence frequency (Total Spike Number per min) as the number of spikes collected per minute on all sensing electrodes in a sample; the single electrode cell number (Cell Number) as the number of cells on a single sensing electrode; the single electrode spike occurrence frequency (Spike Number per min) as the number of spikes collected per minute on a single sensing electrode. The first two statistics reflect the overall cell number and discharge activity of a sample over a period of time, and the latter two statistics reflect the local spatial distribution of cells and spikes.

[0130] In a specific implementation, Figure 9 As shown, Figure 9 The results of the relationship between the number of cells and the frequency of spikes provided in the examples of this application are shown in the figure; wherein, Figure 9Figure a shows the relationship between the total cell number and the total spike frequency. Green dots are outliers and are not included in the regression analysis. Light blue bars are 95% confidence intervals. The Pearson correlation test shows that the linear correlation between the two variables is greater than 98%, with p < 0.05 (unless otherwise stated, R and p described below default to the correlation and significance of the Pearson correlation test). This indicates that the relationship between the total cell number and the total spike frequency shows a significant linear correlation, which is consistent with the previous conclusion that cell number and spike frequency increase with culture time. Figure 9 Figure b shows the relationship between the number of single-electrode cells and the frequency of spikes, R<0.3, p<10-7, indicating that there is a very strong confidence that there is a very weak linear relationship between the two; in other words, there is a high probability that there is a correlation between the two, but it may not be a linear correlation.

[0131] After analyzing the above results, we can draw the following conclusions: the firing frequency varies between cells, and the proportion of each type of cell remains basically the same. It is reasonable to assume that there are n types of cells in a glioma sample, and the firing frequency of each type of cell is constant. If n is 1, then at any location in the sample, the number of cells and the frequency of spikes are linearly positively correlated. However, if n is greater than 1, then the frequency of spikes at a certain point in space is linearly related to the number of cells of each type nearby. When counting the number of cells near this point, there are different possibilities of linear superposition of various types of cells. This results in the possibility of multiple spike frequencies for the same number of cells, showing a nonlinear correlation. On the other hand, since the proportion of each type of cell remains basically unchanged, when counting the spike frequency and cell number of the entire sample, the differences between cells are smoothed out, and thus the number of cells and the frequency of spikes show a linear relationship.

[0132] In one embodiment, determining the migration characteristics of the tumor cells according to the statistical results and the peak occurrence frequency of each sensing electrode in S150 may include:

[0133] S510: Determine an average value of the peak occurrence positions in the migration channel within a preset statistical period according to the peak occurrence frequency of each sensing electrode.

[0134] S511: Determine an average value of the positions of all tumor cells in the migration channel at a preset statistical moment according to the statistical result.

[0135] S512: Analyze the relationship between the spatial distribution of the tumor cells and the frequency of spike occurrence based on the average value of the positions of the spikes in the migration channel and the average value of the positions of all tumor cells in the migration channel.

[0136] In this embodiment, based on the pre-acquired statistical results and the spike occurrence frequency of each sensing electrode, the present application can also use the spike occurrence center and the cell growth center and the difference between the two to characterize the spatial distribution centers of the two. The spike occurrence center (SOC) is defined as: the average value of the position where the spike occurs in the channel over a period of time. The "position" here refers to the vertical distance from a certain point to the starting line of the channel (that is, the edge line at the beginning of cell migration). Assuming that the location of the spike follows a normal distribution, SOC represents the location with the highest probability of the spike occurring. Since the electrodes are distributed linearly along the channel, and the electrode number increases from the proximal end to the distal end of the migration channel, the SOC can be estimated by the weighted average of the number of the electrode where the spike is located.

[0137] Next, the present application may define the cell growth center (CGC) as: the average value of the positions of all cells in the channel at a certain moment. The meaning of "position" is consistent with the definition of SOC. CGC represents the position with the highest probability of existence when the cell position obeys the normal distribution. Finally, the present application may also define the displacement from cell growth center to spike occurrence center (DCS) as: the displacement from the cell growth center to the spike occurrence center in the channel at a certain moment. In the present application, DCS is directional, DCS>0 indicates that SOC is at the far end of the migration of CGC, and DCS<0 indicates the opposite result.

[0138] In a specific implementation, Figure 10 As shown, Figure 10 The results of the relationship between the spatial distribution of cells and the frequency of spikes provided in the examples of this application are shown in the figure; wherein, Figure 10 Figure a shows the relationship between the spatial distribution of glioma cells and the frequency of spikes: the relationship between SOC and CGC over time, and the error bars are the sample standard deviations; the results show that both SOC and CGC show a logarithmic growth trend over time, and SOC is always slightly higher than CGC. Figure 10 Figure b shows that SOC and CGC are linearly related, and the results show a significant strong linear correlation. Figure 10 Figure c shows the DSC frequency histograms for each sample at different times, showing a clear Gaussian distribution with an expected value of 354 μm and a standard deviation of 1.4. This means that there is a 97.5% probability that the spike center will occur distal to the cell growth center. Ultimately, considering the relationship between glioma cell number and spike frequency, it can be inferred that the spike center is a potential marker for the direction of glioma cell invasion.

[0139] In one embodiment, the calculation formula for determining the average value of the peak occurrence positions in the migration channel within a preset statistical period based on the peak occurrence frequency of each sensing electrode is:

[0140]

[0141] Where i is the number of sensing electrodes, n is the number of sensing electrodes, is the frequency of spikes on the i-th sensing electrode, and d is the center distance between adjacent electrodes.

[0142] The calculation formula for determining the average value of the positions of all tumor cells in the migration channel at a preset statistical moment based on the statistical results is:

[0143]

[0144] Where i is the number of sensing electrodes, n is the number of sensing electrodes, is the number of cells on the i-th sensing electrode, and d is the center distance between adjacent electrodes.

[0145] When the above calculation formula is used to determine and After that, this application can also calculate DCS by the following formula, as follows:

[0146]

[0147] After calculation using the above formulas (5) to (7), it can be determined whether the spike generation center appears at the distal end or proximal end of the migration center of the cell growth center.

[0148] The cell migration characteristics analysis device provided in an embodiment of the present application is described below. The cell migration characteristics analysis device described below and the cell migration characteristics analysis method described above can be referenced to each other.

[0149] In one embodiment, Figure 11 As shown, Figure 11 This is a schematic diagram of the structure of a tumor cell migration characteristic analysis device provided in an embodiment of the present application. The present application also provides a tumor cell migration characteristic analysis device, which may include an image acquisition module 210, a cell statistics module 220, a power calculation module 230, a spike signal extraction module 240, and a migration characteristic analysis module 250, specifically including the following:

[0150] The image acquisition module 210 is used to obtain a fluorescent microscopic image corresponding to the tumor cells in the migration channel at each preset acquisition time, wherein the migration channel is a cell migration channel on the microfluidic electrode array chip connected to the culture chamber, extending from the first sensing electrode to the last sensing electrode and extending to the reference electrode.

[0151] The cell statistics module 220 is used to count the number of cells on each sensing electrode in each fluorescence microscopy image and obtain statistical results.

[0152] The power calculation module 230 is used to collect electrophysiological signals generated by tumor cells during migration, which are captured by each sensing electrode on the microfluidic electrode array chip within a preset collection period, and to obtain a power spectral density distribution corresponding to the electrophysiological signals using a fast Fourier transform.

[0153] The spike signal extraction module 240 is configured to extract the spike signal corresponding to each sensing electrode from the electrophysiological signal based on the statistical result and the power spectrum density distribution, and determine the spike occurrence frequency of each sensing electrode.

[0154] The migration characteristic analysis module 250 is configured to determine the migration characteristics of the tumor cells based on the statistical results and the peak occurrence frequency of each sensing electrode.

[0155] In the above embodiment, when analyzing the migration characteristics of tumor cells, since the migration channel of the present application is a cell migration channel connected to the culture chamber on the microfluidic electrode array chip, extending from the first sensing electrode to the last sensing electrode and to the reference electrode, a fluorescence microscopic image corresponding to the tumor cells in the migration channel at each preset acquisition time can be first obtained. In this way, the number of cells on each sensing electrode in each fluorescence microscopic image can be counted and a statistical result can be obtained. Next, the present application can also collect electrophysiological signals generated by the tumor cells during migration, captured by each sensing electrode on the microfluidic electrode array chip within a preset acquisition period, and use fast Fourier transform to obtain a power spectral density distribution corresponding to the electrophysiological signal. In this way, based on the statistical results and the power spectral density distribution, the spike signal corresponding to each sensing electrode can be extracted from the electrophysiological signal, and the spike occurrence frequency of each sensing electrode can be determined. Then, based on the statistical results and the spike occurrence frequency of each sensing electrode, the migration characteristics of the tumor cells can be determined, such as analyzing the relationship between the number of cells and the spike occurrence frequency, the relationship between the spatial distribution of cells and the spike occurrence frequency, etc., thereby studying the migration characteristics of tumor cells in multiple aspects.

[0156] In one embodiment, the present application also provides a computer-readable storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the tumor cell migration characteristic analysis method as described in any of the above embodiments.

[0157] In one embodiment, the present application further provides a computer device, including: one or more processors, and a memory.

[0158] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method for analyzing tumor cell migration characteristics as described in any one of the above embodiments are performed.

[0159] Schematically, as Figure 12 As shown, Figure 12 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 can be provided as a server. Figure 12 Computer device 300 includes a processing component 302, which further includes one or more processors, and a memory resource represented by memory 301 for storing instructions executable by processing component 302, such as an application. The application stored in memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, processing component 302 is configured to execute the instructions to perform the tumor cell migration characteristic analysis method of any of the above-mentioned embodiments.

[0160] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or the like.

[0161] Those skilled in the art will understand that Figure 12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0162] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0163] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0164] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing tumor cell migration characteristics, characterized in that: The method comprises: Acquire a fluorescence microscopic image corresponding to the tumor cells in the migration channel at each preset acquisition time, wherein the migration channel is a cell migration channel on the microfluidic electrode array chip connected to the culture chamber, extending from the first sensing electrode to the last sensing electrode and extending to the reference electrode; Counting the number of cells on each sensing electrode in each fluorescence microscopy image and obtaining statistical results; collecting electrophysiological signals generated by tumor cells during migration, captured by each sensing electrode on the microfluidic electrode array chip within a preset collection period, and obtaining a power spectral density distribution corresponding to the electrophysiological signals using fast Fourier transform; extracting spike signals corresponding to the respective sensing electrodes from the electrophysiological signals based on the statistical results and the power spectrum density distribution, and determining the spike occurrence frequency of the respective sensing electrodes; The migration characteristics of the tumor cells are determined according to the statistical results and the peak occurrence frequency of each sensing electrode.

2. The method for analyzing tumor cell migration characteristics according to claim 1, wherein: The step of extracting the spike signal corresponding to each sensing electrode from the electrophysiological signal based on the statistical result and the power spectrum density distribution, and determining the spike occurrence frequency of each sensing electrode, includes: determining an energy concentration frequency range of the electrophysiological signal based on the power spectral density distribution; Filtering the electrophysiological signal using a filter, and extracting a signal to be processed corresponding to the energy concentration frequency range from the filtered electrophysiological signal; The peak signal corresponding to each sensing electrode is extracted from the signal to be processed based on the statistical result, and the peak occurrence frequency of each sensing electrode is determined.

3. The method for analyzing tumor cell migration characteristics according to claim 2, wherein: The extracting the peak signal corresponding to each sensing electrode from the signal to be processed based on the statistical result and determining the peak occurrence frequency of each sensing electrode includes: Determine the correction threshold when extracting spike signals; Use the findpeaks function in Matlab to find the extreme value of the signal to be processed whose absolute value is greater than the correction threshold; After eliminating the extreme values ​​that do not meet the preset extreme value specifications from the found extreme values, an initial peak signal corresponding to each sensing electrode is obtained; After removing abnormal peaks from each initial peak signal according to the statistical result, the peak occurrence frequency of each sensing electrode is determined according to the preset acquisition period and the initial peak signal after removing the abnormal peaks.

4. The method for analyzing tumor cell migration characteristics according to claim 3, wherein: The calculation process of determining the correction threshold when extracting the peak signal is as follows: Among them, in formula (1) to formula (4), and are the arithmetic mean and standard deviation of the electrophysiological signals, The experience value is 4, , is the electrophysiological signal captured by the i-th sensing electrode, .

5. The method for analyzing tumor cell migration characteristics according to claim 1, wherein: Determining the migration characteristics of the tumor cells according to the statistical results and the peak occurrence frequency of each sensing electrode includes: Determine the total number of cells on all sensing electrodes and the total spike frequency collected per minute on all sensing electrodes based on the statistical results and the spike frequency of each sensing electrode; The relationship between the number of cells of the tumor cells and the spike frequency is analyzed based on the number of cells on each sensing electrode and the corresponding spike frequency, the total number of cells on all sensing electrodes and the corresponding total spike frequency.

6. The method for analyzing tumor cell migration characteristics according to claim 1, wherein: Determining the migration characteristics of the tumor cells according to the statistical results and the peak occurrence frequency of each sensing electrode includes: Determining, according to the peak occurrence frequency of each sensing electrode, an average value of the peak occurrence positions in the migration channel within a preset statistical period; determining an average value of the positions of all tumor cells in the migration channel at a preset statistical moment according to the statistical results; The relationship between the spatial distribution of the tumor cells and the frequency of spike occurrence is analyzed based on the average value of the positions of the spikes in the migration channel and the average value of the positions of all tumor cells in the migration channel.

7. The method for analyzing tumor cell migration characteristics according to claim 6, wherein: The calculation formula for determining the average value of the peak occurrence positions in the migration channel within a preset statistical period based on the peak occurrence frequency of each sensing electrode is: Where i is the number of sensing electrodes, n is the number of sensing electrodes, is the frequency of spikes on the i-th sensing electrode, and d is the center distance between adjacent electrodes; The calculation formula for determining the average value of the positions of all tumor cells in the migration channel at a preset statistical moment based on the statistical results is: Where i is the number of sensing electrodes, n is the number of sensing electrodes, is the number of cells on the i-th sensing electrode, and d is the center distance between adjacent electrodes.

8. A device for analyzing tumor cell migration characteristics, characterized in that: include: An image acquisition module, configured to acquire a fluorescence microscopic image corresponding to tumor cells in the migration channel at each preset acquisition moment, wherein the migration channel is a cell migration channel on the microfluidic electrode array chip connected to the culture chamber, extending from the first sensing electrode to the last sensing electrode and extending to the reference electrode; The cell statistics module is used to count the number of cells on each sensing electrode in each fluorescence microscopy image and obtain statistical results; a power calculation module for collecting electrophysiological signals generated by tumor cells during migration, captured by each sensing electrode on the microfluidic electrode array chip within a preset collection period, and obtaining a power spectral density distribution corresponding to the electrophysiological signals using a fast Fourier transform; a spike signal extraction module, configured to extract, from the electrophysiological signal, spike signals corresponding to the respective sensing electrodes based on the statistical results and the power spectrum density distribution, and determine the spike occurrence frequency of the respective sensing electrodes; The migration characteristic analysis module is used to determine the migration characteristics of the tumor cells according to the statistical results and the peak occurrence frequency of each sensing electrode.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method for analyzing tumor cell migration characteristics according to any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method for analyzing tumor cell migration characteristics according to any one of claims 1 to 7 are performed.

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